Characterization and Exploration of the Flavor Profiles of Green Teas from Different Leaf Maturity Stages of Camellia sinensis cv. Fudingdabai Using E-Nose, E-Tongue, and HS-GC-IMS Combined with Machine Learning

Understanding how leaf maturity affects the flavor attributes of green tea is crucial for optimizing harvest timing and processing strategies. This study comprehensively characterized the flavor profiles of Fudingdabai green teas at three distinct leaf maturity stages-single bud (FDQSG), one bud + one leaf (FDMJ1G), and one bud + two leaves (FDTC2G)-using a multimodal approach integrating electronic nose, electronic tongue, HS-GC-IMS, relative odor activity value (rOAV) evaluation, and machine learning algorithms. A total of 85 volatile compounds (VOCs) were identified, of which 41 had rOAV > 1. Notably, 2-methylbutanal, 2-ethyl-3,5-dimethylpyrazine, and linalool exhibited extremely high rOAVs (>1000). FDQSG was enriched with LOX (lipoxygenase)-derived fresh, grassy volatiles such as (Z)-3-hexen-1-ol and nonanal. FDMJ1G showed a pronounced accumulation of floral and fruity compounds, especially linalool (rOAV: 7400), while FDTC2G featured Maillard- and phenylalanine-derived volatiles like benzene acetaldehyde and 2,5-dimethylfuran, contributing to roasted and cocoa-like aromas. KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis revealed significant enrichment in butanoate metabolism and monoterpenoid biosynthesis. Random forest-SHAP analysis identified 20 key flavor markers, mostly VOCs, that effectively discriminated samples by tenderness grade. ROC-AUC validation further confirmed their diagnostic performance (accuracy ≥ 0.8). These findings provide a scientific basis for flavor-driven harvest management and the quality-oriented grading of Fudingdaibai green tea.